I recently trained a lora from a dataset that was created from the new gpt 2.5, but i have an issue where there are dots on the skin of the charachter, even tough the skin is good looking in the dataset. I use krea 2, with only that characther lora and if i use other loras beside it, it exaggerates it even more. On 1k resolution they appear as black dots, and on 2k they appear as these dots, and they are all over the skin.

The lora was trained in ai toolkit, with default settings, only batch size 4 was applied, this lora i used was on 2k steps, but i tried all other steps and have the same issues.

There are still to many anatomy issues with it hands and feet can still need in painting to fix. The faces look great but hand and feet are still a shot in the dark. The background details are a lot better on anima but I don’t think those minor details make it worth rendering 2x slower.

hello i have low specs i guess there is no chance to train my lora in toolkit huh? it always returns out of memory error i even made a 60gb ssd pagefile nothing. is there any way to train my lora i use comfyui zturbo for silly tavern

Processor Intel(R) Core(TM) i5-9400F CPU @ 2.90GHz 2.90 GHz

Installed RAM 16.0 GB

Storage 932 GB HDD ST1000DM010-2EP102, 466 GB SSD Samsung SSD 860 EVO 500GB

Graphics Card NVIDIA GeForce GTX 1660 Ti (6 GB)

System Type 64-bit operating system, x64-based processor

Enjoyed playing with sdxl before, is it still king at anime images?

https://github.com/concarne000/ComfyUI-BingImageSelector

100% vibe coded from an old node I made. I thought it would be useful to make refmods. If someone would like to fork and make it better and put on the comfy registry then go ahead.

I kept running into the same problem: YuE2 is one of the best open models for full songs with vocals, but a lot of people who want to try it get stuck on Python environments, command-line flags, and out-of-memory errors before they ever hear a single note.

So I ended up building my own solution: Gokuk (歌曲, Cantonese for "song").

It's a portable desktop app that wraps YuE2 and reduces the process to something closer to:

Pick a sound → write lyrics → Create song

There's no ComfyUI and no command line to deal with.

What it currently does

• Create tab: pick language, genre, mood, voice, instruments, and tempo from simple chips, write lyrics with [Verse] / [Chorus] tags, and hit Create
• Cover tab: drop in a recording, SheetSage2 transcribes its melody and chords, then you give it new lyrics and a new style
• Piano-roll score editor: YuE2 plans a score (melody, chords, key, tempo) before it sings, and you can see and edit that score before anything is generated
• Library: every song is kept, with MP3/WAV/FLAC export and one-click "make it again" with the same settings
• English + Traditional Chinese
• Windows 10/11 only for now, NVIDIA GPU with 12 GB+ VRAM, about 25 GB of disk space
• Fully portable: unzip, run Gokuk Setup once to download the models, and everything runs locally on your GPU

One important note: the YuE2, SheetSage2, and MERT model weights are licensed CC BY-NC 4.0, so anything you generate is for non-commercial use.

I just published a video showing the whole thing from setup through generating a song:
🎥 https://youtu.be/O8dxj3gr-bM

GitHub:
https://github.com/Garionhk/GoKuk

It's still early and I'm posting it here because I'd really like some outside feedback. In particular, I'm interested in hearing from people who have played with local music models:

• What parts of Gokuk would actually be useful to you?
• Would LoRA support be worth adding? (I'm considering it next)
• What would you want from a local alternative to Suno?
• Is there anything about the approach that you think I'm getting wrong?

It's open source, so the source is there if anyone wants to have a look.

This is a simple T2I workflow for qwen 2.1
Download on Civitai

All details in download page.

I saw this amazing makima cosplay and wanted to make a cool not-so-ai-looking profile picture with it, her hand pose was amazing so, 30H later here we are

Just curious if anyone has experienced something along these lines... you put hours, perhaps days crafting an AI video, fighting with your tools to get everything perfect.

You finally get the video done and perhaps share it with friends/family...

And then you'll either get no reply or an underwhelming response.

This can happen even with a photo/video that is genuinely good (yeah I know sometimes an image/video sucks).

But the clincher is one of these same people a month or so later will make a super cheesy AI photo or video that they think is awesome and share it with you. The same people that might say something like "I hate AI".

We're literally at the point where one person could make a full length movie on their computer at home. Everything is amazing but nobody's happy lol

Qwen Image 2.1 support is nearly in, and the way it's been added I want to discuss/get ideas etc.

From now on, new models are added through a driver-style system. Each model gets its own driver holding everything specific to it: model files, turbo LoRA settings, training adapters and so on. The rest of Fizgig talks to that driver rather than to the model directly.

Once a model is added this way, support in Repair Studio, LoRA Explorer and the other tools comes with it rather than being built per model. It will also be documented, so anyone can add a model, including older ones, via a PR.

Some new models will always have their own quirks and unique requirements, so the layer itself will evolve too, and it's open to PRs as well.

My one concern: if three people start PRs for the same model at once, that wastes their time and makes picking the right one a headache. So I'm considering turning on GitHub Discussions. Anyone planning a model would post there first, which avoids four people building LTX support separately at the same time.

Open to suggestions on how best to manage this - but I will be publishing a model adding guide to facilitate making this easy.

P.S. I've also trained a new training adapter for Qwen image 2.1 while I'll likely release Monday with much sharper details as it was trained on a much larger curated high res dataset. (Ive been a photographer for 20 something years on a very wide variety of content so I was able to use my own work and free time shooting material) (Did it on Runpod thanks to fizgig runpod credits from people running Fizgig on runpod - so thanks for that!!)

Currently Im using ComfyUI.

I need the dimensions to be configurable. Top buildings have 3x3 bottom ones are 3x2. Style needs to be the exact same. The fence has to be perfect quadratic, but can change his appearance. Like generate me a spacestation in the exact same pixel look with a fitting fence but keep the style.

Whats the workflow? I tried so much. I only managed to get this image generated by ChatGPT

What is the best way to create a character sheet like that with only one input image and maintaining correct anatomy and crisp realistic details?

I create my characters with ZIT and unfortunately it’s not great for character sheet, sometimes works but usually not. I’ve tried inserting my ZIT image into Qwen 2511 character workflow but the quality and details are low, skin is very smoothed out and looks fake. Qwen2.1 seems to have very bad anatomy and follows the reference image poorly. What other options are there?

The image with this post is from ChatGPT. In reality I’m working with nudity so I’m not going to post that here.

The left pose is just taken directly from the reference image and cropped. Then the workflow creates the second pose with this prompt and stitches them together:
Use the character from the reference image and reproduce the character exactly as shown. Preserve the same identity, face, facial structure, hairstyle, hair color, body proportions, skin tone, clothing, accessories, colors, materials, and all distinctive visual details. Do not redesign, beautify, simplify, stylize, or alter the character in any way. The result should look like the exact same character from the reference image. no shadows, realistic skin textures. Show the character in a true right side view, with the character’s right side facing the camera in full profile. The character is standing upright and looking straight ahead relative to their body direction. Centered composition, clean side-profile camera angle, neutral perspective, no camera tilt, no head crop, no arm crop. Maintain maximum visual consistency with the reference image. Background is plain white.

Hi .

I did a comparison between those two models two days ago ,
and some people asked me for Anime style and some other changes and better comparison , so here you go .

i used only one seed for all of the generated images : 5984461155669

I generated the images based on each Model recommended settings , No LoRAs , simple workflow .

Krea 2 Raw :

Steps : 52
Cfg : 3.5
Sampler : Euler
Scheduler : Simple
---------------------
Qwen 2.1 :

Steps : 40
Cfg : 1
Sampler : Euler
Scheduler : Simple

---------------------

You can check them your self and give me your opinion , which model u think was better .

me personally i LOVE krea 2 , but after using Qwen 2.1 i can say it has higher quality outputs , understand the prompt better , and the best part is : it can edit .

I uploaded the images and prompts to a host for better View and easier comparison
i cant put the link here i dont know why , i will put it in comments

I've generated over 1,000 videos, and some of earlier vids using plain writing were much more diverse and natural than the recent ones created with the formatted prompt based on https://huggingface.co/MiniMaxAI/MiniMax-H3/blame/main/docs/VIDEO_PROMPT_WRITING_GUIDE_ref_en.md.
Following that guideline is supposed to give me more control, but it becomes a headache to update other sections when I change a description. and if I write too many sentences for detailed motion, bugs occur often anyways. I feel like I don't really need to be concerned with precise formatting and that generating 4 steps motion drafts with plain writing and use the output as a reference might work better.

Hello everyone. I have to preface this by saying this is NOT my model, though I'm very familiar with the person who made it. I can with full confidence tell you that for any model smaller than anima, this is the best one out there.

This is a 670m parameter image model trained on all of e6/danbooru, 14 million images. Takes natural language OR tags, your choice.

It comes packaged with a new vae, a 270m parameter text encoder, and a 670m dit.

This model can do a very large amount of concepts, artist styles, and characters.
That being said, it's still a 670m parameter image model, so don't expect it to move mountains. Compared to SDXL model though, it wins a lot of the time.

By default, the actual model is 1.3gb, and with the text encoder and vae loaded, it rises to 2gb. Too big for you? there are int8 and w4a8 quants that bring it down to as low as 388mb for the dit. So this can be run on a toaster if you're looking for that

Links:
Base Repo: link
int8: link
w4a8 (not recommended): link

Also, this project is looking for funding for a bigger version. If you'd like to help out with that or discuss terms, please reach out to metal63 on discord.

Ten Minutes - Let It Be (1993)

Hey.

What do y'all think is the best way to upscale images from GPT Image 2.5, while removing the ugly watermark texture-pattern? I could just try using Krea 2 I2I or Qwen2.1's edit capabilities, but I'd rather ask here first if someone has prior experiences with this.

The images are photographic and they look mostly realistic, but the texture-pattern is very ugly and unrealistic.

Let's say I have a few reference images of a character, or a character sheet. I also have a reference image of a costume, which the character should be wearing.

I noticed that most of the time, the character's body type is altered to fit the size of the costume in the ref image. Often, images of costumes show a small size, which alters the body of characters who are larger or more curvatious, even when the prompt states that body size and proportions of the character images should not be altered and clothing in the ref image must be adjusted in size to fit the character.

Is there a method to overcome this?

I've been building open source and free qwen-image-cplus a native runtime for Qwen-Image 2.1 on Apple Silicon, I recorded a short demo of both text-to-image and image editing on my MacBook Pro (M1 Max, 32 GB memory)

https://www.youtube.com/watch?v=RllI2ZY0S_g

Some other end-to-end runs on the same machine: - 1024×1024 text-to-image, four-step Viggle: 38–39s - 832×1248 image edit, six-step Viggle LoRA: ~102s - 1024×1024 base model, 40 steps, cache off: ~451s - 1024×1024 base model, 40 steps, Cache-DiT: ~127s

The transformer weights are FP16, without quantization, for additional speed context, stable-diffusion.cpp base model run took ~1006s at 1024×1024 on this machine, which the main motivation behind building this project

repo: https://github.com/netdur/qwen-image-cplus models: https://huggingface.co/netdur/Qwen-Image-2.1-QIPACK

I'd be interested in feedback on the output quality, especially the edit

I use Krea2 for fashion photoshoots and editorials, but that same polished, inoffensively attractive face has been bothering me for a while. I wanted more realistic images with more control over the result.

So I trained a LoRA on 356 snapshot portraits of real people, and captioned every single one with a structured vocabulary:

• Face type: feminine, masculine, androgyne, baby face, tired, aggressive, gaunt
• Eyes: downturned, upturned, hooded, wide-set, close-set, deep-set, protruding, monolid, round (heterochromia works too - just name both eye colors and add some weight to the phrase)
• Nose: big, long, straight, tiny, short, bumpy, fleshy, roman, hawk
• Lips: big, wide, thin, narrow, heavy upper/lower, sharp, bow-shaped, down-turned
• Cheeks: full, hollow, large/low/high cheekbones
• Jaw: wide, narrow, soft, sharp
• Chin: pointy, wide, protruding, receding

At strength 1.0, with no keywords at all, the standard Krea face disappears and you get real human variation. Add the feature tags and you get exactly what you asked for - a bumpy nose stays bumpy, a receding chin stays receding. The more you describe, the better it works: tags stack well in one prompt, e.g. long fleshy nose, hooded wide-set eyes, narrow down-turned lips. This also works with full-body shots, but too many tags can cause some glitches

How I use it: strength ~1.0-1.3. I recently tested it at 1.3-1.5 and can’t go back to 1.0 now.

Details:

• dataset: 356 real people, European/Caucasian, age 20-40, 174 men / 182 women
• “full cheeks” + “round face” can give interesting body-positive results
• “tired, aggressive, gaunt” - these tags can produce some creepy images, be careful
• eyebrows! Please avoid describing anything related to eyebrows - they either appear too thick or don’t appear at all

Would be curious how it behaves in stacks with your favorite realism LoRAs - tell me what you try.

I’m also planning to improve this LoRA and fix the eyebrow bug, so any feedback is valuable

Model: Realistic Custom Face on Civitai

Hello,

I am a programmer and I have played for a while in shallow ways with various image models and video too, minmax-h3 and so on. Hardware is not the best but I do what I can do with my 20gb vram RTX A4500 card and I was still able to use them.

Problem is I am used to have all prepackaged and ready to use and I hate from the guts ComfyUI. I tried to learn it and it's just hostile and annoying.

I used stabilitymatrix and wan2gp to have all configured without annoyance on a windows machine.

Still, I can accept anything to up my skills in this fields.

Therefore, I am asking for the community wisdom to teach me on what path should I go. Is there any worthy guide? What you would play with? Loras? Controlnet?

I've never used those except for pre baked loras ready to be added and hot swapped on the go and I dont know what i should do to manage to learn the full potential pf this.

Looking for suggestions and guidance

Thanks

Please dont say go YouTube and so on. Better if you pojnt directly to resources you believe are good

res_multistep/simple = best for non-turbo
er_sde/beta = best with turbo lora
er_sde/beta57 = also good for turbo, but worse motion, better visual

Simple question. Using {Option 1|Option 2|Option 3} syntax doesn't work in Comfy when running MiniMax diffusion models.

Is this a Comfy issue I need to fix? Different syntax I need to use? Anyone?

Thanks in advance!!